Coordinated neighborhood energy sharing using game theory and multi-agent systems

In this paper, a decentralized control algorithm is presented for coordinated energy sharing among smart homes in neighborhood areas using a game-theoretic approach and a multi-agent system (MAS). The aim of the study is to reduce the electricity bill of end-users with dynamic pricing where price is associated to aggregated consumption. To reduce the cost of consumption, a control algorithm performs home appliance scheduling and battery control while enabling energy sharing among neighbors in the neighborhood. We assume that photovoltaic (PV) and battery systems are installed in smart homes and end-users are decision-makers willing to optimize the run time of electricity appliances and the control inputs of the battery. In particular, end-users aim to schedule controllable appliances and/or decide about battery charging during low price hours and discharging during high price hours. The battery can be charged by three strategies: using local PV generation, from neighborhood residual generation and grid energy jointly or distinctly. In this study, a MAS is used for modeling entities (homes and aggregator) in the neighborhood as agents. The aggregator agent is the supervisor agent which determines the aggregated profile and dynamic price by communicating with home agents. Home agents are independent and selfish decision-makers which only focus on the maximization of their own welfare while achieving near-optimal performance at Nash equilibrium of a formulated non-cooperative coordination game. Results show that each smart home can benefit from this scheme, compared to a baseline (no control) scenario, as well as reduce the neighborhood total cost and peak load consumption.